Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009.  The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km-1) data (Hubbert and Bringi 1995).    ARMOR file naming conventions are as follows:    RAW_NA_000_125_20090410171216.gz RAW: file format 125: can scan type, where 125 indicates a full or sector volume plan position indicator  20090410171216: date and time in the order of year, month, day, hour, minute, and second   ARMOR_20090410171216_qc1.uf.gz ARMOR: radar name 20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS) qc1: denotes ARMOR processed data uf: denotes the file format    NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics. Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi2 value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x).    Hourly file naming conventions are as follows:   LYLOUT_090410_160000_3600.dat.gz LYLOUT: LMA file designator 090410: date in order of last two digits of year (YY), month (MM), and day (DD) 160000: time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   Acknowledgments:  Data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576.

opencc-by-4.0Jul 2020View details →

ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"

Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below.    The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004).  Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi2 value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset.    The hourly fine naming conventions are as follows for the February 2008 data: LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz LMA_NA: LMA file designator corresponding to the NALMA 2008-02-06: year (YYYY)-month (MM)-day (DD) 10-00-00: UTC time, (HH)-minute (MM)-second (SS)   And for the April 2008 data: LYLOUT_080411_180000_3600.dat.gz LYLOUT: LMA file designator 080411: date in order of last two digits of year (YY), month (MM), and day (DD) 180000: UTC time in order of hour (HH), minute (MM), and second (SS) 3600: length of period covered in file in seconds (3600 s = 1 hour)   ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s-1), spectrum width (m s-1), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km-1) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).   Acknowledgments:  NALMA data were collected with support from NASA MSFC Award NNM05AA22A.   References: Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 1906–1915. https://doi.org/10.1109/36.951081 Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. Journal of Atmospheric and Oceanic Technology, 12, 643–648.  Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. Journal of Atmospheric and Oceanic Technology, 21(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2 Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. Geophysical Research Letters, 26(23), 3573–3576. Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. Geophysical Research Letters, 28(1), 143–146. https://doi.org/10.1029/2000GL011464

opengpl-3.0-or-laterOct 2024View details →

Vertical Profiles of Convection-Permitting Simulations for Predicting Thunderstorm Occurrence

This repository contains datasets for training and evaluation of the machine learning (ML) models in K. Vahid Yousefnia et al., Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model, 2024 (submitted to Artificial Intelligence for the Earth Systems, preprint available at https://arxiv.org/abs/2409.20087).

opencc-by-4.0Dec 2022View details →

Thunderstorm outflows in the Mediterranean Sea area

In the context of the European projects “Wind and Ports” (grant No. B87E09000000007) and “Wind, Ports and Sea” (grant No. B82F13000100005), an extensive in-situ wind monitoring network was installed in the main ports of the Northern Mediterranean Sea. An unprecedent number of wind records has been acquired and systematically analyzed. Among these, a considerable number of records presented non-stationary and non-Gaussian characteristics that are completely different from those of synoptic extra-tropical cyclones, widely known in the atmospheric science and wind engineering communities. The cross-checking with meteorological information allows to identify which of these events can be defined as thunderstorm winds, i.e., downbursts and gust fronts. The scientific literature of the last few decades has demonstrated that downbursts, and especially micro-bursts, are extremely dangerous for the natural and built environment. Furthermore, recent trends in climate change seem to preview drastic future scenarios in terms of intensification and frequency increase of this type of extreme events. However, the limited space and time structure of thunderstorm outflows makes them still difficult to be measured in nature and, consequently, to build physically reliable and easily applicable models as in the case of extra-tropical cyclones. For these reasons, the collection and publication of events of this type represents a unique opportunity for the scientific community. The dataset here presented was built in the context of the activities of the project THUNDERR “Detection, simulation, modelling and loading of thunderstorm outflows to design wind-safer and cost-efficient structures”, financed by the European Research Council (ERC), Advanced Grant 2016 (grant No. 741273, P.I. Prof. Giovanni Solari, University of Genoa). It collects 29 thunderstorm downbursts that occurred between 2010 and 2015 in the Italian ports of Genoa (GE) (4), Livorno (LI) (14), and La Spezia (SP) (11), and were recorded by means of ultrasonic anemometers (Gill WindObserver II in Genoa and La Spezia, Gill WindMaster Pro in Livorno). All thunderstorm events included in the database were verified by means of meteorological information, such as radar (CIMA Research Foundation is gratefully acknowledge for providing with most of the radar images), satellite, and lightning data. In fact, (i) high and localized clouds typical of thunderstorm cumulonimbus, (ii) precipitations, and (iii) lightnings represent reliable indicators of the occurrence of the thunderstorm event. Some events were recorded by multiple anemometers in the same port area – the total number of signals included in the database is 99. Despite the limited number of points (anemometers), this will allow the user to perform cross-correlation analysis in time and space to eventually retrieve size, position, trajectory of the storm, etc. The ASCII tab-delimited file ‘Anemometers_location.txt’ reports specifications of the anemometers used in this monitoring study: port code (Port code – Genoa-GE, Livorno-LI, La Spezia-SP); anemometer code (Anemometer code); latitude (Lat.) and longitude (Lon.) in decimal degree WGS84; height above the ground level (h a.g.l.) in meters; Instrument type. Bi-axial anemometers were used from the ports of Genoa and La Spezia, recording the two horizontal wind speed components (u, v). Three-axial ultrasonic anemometers were used in the port of Livorno, also providing the vertical wind speed component w (except bi-axial anemometers LI06 and LI07). All anemometers acquired velocity data at sampling frequency 10 Hz, sensitivity 0.01 m s-1 (except anemometers LI06 and LI07 with sensitivity 0.1 m s-1) and were installed at various heights ranging from 13.0 to 75.0 m, as reported in the file ‘Anemometers_location.txt’. The ASCII tab-delimited file ‘List_DBevents.txt’ lists all downburst records included in the database, in terms of: event and record number (Event | record no.); port code (Port code); date of event occurrence (Date) in the format yyyy-mm-dd; approximate time of occurrence of the velocity peak (Time [UTC]) in the format HH:MM; anemometer code (Anemometer code).  The database is presented as a zip file (‘DB-records.zip’). The events are divided based on the port of occurrence (three folders GE, LI, and SP). Within each folder, the downburst events that were recorded in that specific port are reported as subfolders (name format ‘[port code]_yyyy-mm-dd’) and contain the single anemometers signals as TAB-delimited text files (name format ‘[port and anemometer code]_yyyy-mm-dd.txt’). Each sub-dataset (file) contains 3(4) columns and 360.000 rows. The first column shows the 10-h time vector (t, ISO format) in UTC, while the remaining 2(3) columns report the 10-h time series of 10-Hz instantaneous horizontal (zonal west-to-east u, meridional south-to-north v) and, where available, vertical (positive upward w) wind speed components, centred around the time of maximum horizontal wind speed (vectorial sum of u and v). The choice of representation of the wind speed in a large time interval (10 hours) allows the user to perform a more comprehensive and detailed analysis of the event by taking into account also the wind conditions before and after the onset of the downburst phenomenon. 'Not-a-Number' (‘NaN’) values are reported in wind velocity signals when the instrument did not record valid data. Some wind speed records show noise in discrete intervals of the signal, which reflects in an increase of the wind speed standard deviation. A modified Hampel filter was employed to remove measurement outliers. For each wind speed signal, every data sample was considered in ascending order, along with its adjacent ten samples (five on each side). This technique calculated the median and standard deviation within the sampling window using the median absolute deviation. Elements deviating from the median by more than six standard deviations were identified and replaced with 'NaN'. The tuning of the filter parameters involved finding a balance between overly agressive and insufficient removal of outliers. Residual outliers were subsequently manually removed through meticulous qualitative inspection. The complexity and subjectivity of this operation provide users with the opportunity to explore alternative approaches. Consequently, the published dataset includes two versions: an initial version (v1) comprising the original raw data with no filtering applied, and a second "cleaned" version (v2). The presented database can be further used by researchers to validate and calibrate experimental and numerical simulations, as well as analytical models, of downburst winds. It will also be an important resource for the scientific community working in the wind engineering field, in meteorology and atmospheric sciences, as well as in the risk management and reductions of losses related to thunderstorm events (i.e., insurance companies).

opencc-by-4.0Oct 2021View details →

Machine learning code and dataset for "Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance"

This repository contains the code and dataset for the paper: Nowcasting thunderstorm hazards using machine learning: the impact of data sources on performance, Natural Hazards and Earth System Sciences, 2022, <a href="https://doi.org/10.5194/nhess-2021-171">https://doi.org/10.5194/nhess-2021-171</a> The GitHub code repository at <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">https://github.com/meteoswiss-mdr/ts-nowcast-datasources</a> may contain a more up-to-date version of the code if bug fixes etc. have been necessary. The file <a href="https://zenodo.org/api/files/41faa1b7-17f6-4a75-be09-7743426ef13c/ts-nowcast-datasources-publication.zip">ts-nowcast-datasources-publication.zip</a> in this Zenodo release contains the status of the GitHub repository at the time of the publication of the paper. For instructions for using the data, please see the <a href="https://github.com/meteoswiss-mdr/ts-nowcast-datasources">code repository</a>.

opencc-by-4.0Apr 2022View details →

Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia

Data includes: <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>

opencc-by-4.0Oct 2024View details →

CLIMAtic investigation of THUNDERstorm winds

In the framework of the EU Project ERIES : Engineering research Infrastrucures for European Synergies, an experimental campaign was carried out at the Jules Verne Climatic Wind Tunnel Thermal Unit SC2 at the Centre Scientifique et Technique du Bâtiment (CSTB) in Nantes, France. The aim of the experimental activity was to assess the geometric and dynamics characteristics of downburst like impinging jet winds by varying the temperature difference ∆ T between the impinging jet flow and the calm surrounding environment. This could be realized at large geometric scales and Reynolds number thanks to the large. The projet associated University of Genoa, Italy and Centre Scientifique et Technique du Bâtiment, Nantes, France. Contact persons : Federico Canepa <a title="mailto:federico.canepa@unige.it" href="mailto:federico.canepa@unige.it" target="_blank" rel="noreferrer noopener">federico.canepa@unige.it</a>, Anthony Guibert, <a title="mailto:anthony.guibert@cstb.fr" href="mailto:anthony.guibert@cstb.fr" target="_blank" rel="noreferrer noopener">anthony.guibert@cstb.fr</a>

opencc-by-4.0Jul 2021View details →

Loon Stratospheric Electrical Measurements above Thunderstorms

Loon LLC telemetry data containing measurements from a corona current sensor and also aligned with lightning indicators from BCI (CDO, CloudHeight) and the Geostationary Lightning Mapper (GLM).  See README for more details.

opencc-by-4.0Mar 2023View details →

Data archive for Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning

This dataset contains the machine learning training data files, pretrained model weights and results for the paper Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning, submitted to Natural Hazards and Earth System Sciences, 2023. The radar dataset can be found at the following Zenodo repository: <a href="https://doi.org/10.5281/zenodo.6325370">https://doi.org/10.5281/zenodo.6325370</a> For instructions for using the data, please see the GitHub code repository at <a href="http://github.com/meteoswiss/c4dl-polar">https://github.com/meteoswiss/c4dl-polar</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory: <ul> <li>Training data (patches_quality-index_2020.zip or patches_*_2020.nc) -> data/2020/</li> <li>Results: (results.zip) -> runs/run*/results/</li> <li>Pretrained models (models_run*) -> runs/run*/</li> </ul>

opencc-by-4.0Apr 2020View details →

Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)

Manuscript submitted to Earth and Space Science AGU entitled with  (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City) by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao The STEC data inside two compressed folders named as stec059 and stec061, respectively. The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year. For example: ES010590.18STEC ES01 is the station name 059  is the day of year (DOY), 2018

opencc-by-4.0Jul 2024View details →

Behavior of Telecommunication Lattice Towers to Thunderstorm Winds

<h1>Dataset Description</h1> This work aims at closing the knowledge gap between the wind field monitoring of real structures and wind tunnel testing by simulating real atmospheric boundary layer (ABL) and thunderstorm events in the Wind Energy, Environment, Engineering (WindEEE) research facility. The real events were acquired by a wind and structural monitoring system installed on a 50 m telecommunication lattice tower located in Sânnicolau Mare, Romania. The study reproduces complex downburst wind systems, in a controlled laboratory environment, like those observed in the field monitoring. The wind-induced response of two typical telecommunication lattice towers of different heights, i.e. 50 m and 90 m is investigated by means of both aerodynamic and aeroelastic tests. The acquired data will allow to compare and calibrate wind tunnel test results with field monitoring structural data measured during intense ABL and thunderstorm winds by the Sânnicolau Mare monitoring system. This extends the wind field and aerodynamic database which can be further utilized for codification purposes and for validating numerical and analytical models. The proposed work aims to advance code-based design of telecom lattice towers to thunderstorm winds. This work involved three areas of testing – wind field characterization to determine the best settings to match full scale / realistic wind loads, aeroelastic tests of both a 90m and 50m full towers (1:100 scale) using strain gauges as well as force balances, and a 1:10 sectional model of the top of the 50m tower to study the aerodynamics of the tower both with and without ancillaries added   <h2>S0. Documentation</h2> Contains information documents regarding the instrumentation specifications, test plan, and other important diagrams.   <h2>S1. Wind Profile Stand</h2> A vertical stand of 11 TFI cobra probes measured wind field data at heights of 50, 100, 150, 200, 300, 400, 500, 600, 700, 800, and 900 mm from the ground surface. For each experiment described below, the Cobra Probe stand was located in select locations to capture the near-surface flow. <h3>E1. ABL Profile Development</h3> The 60-fan wall located on one side of the hexagonal shaped WindEEE test chamber was used to generate the various ABL flows for this experiment. Each fan on this wall is individually controlled allowing a versatile range of ABL flow conditions. In addition, the ABL flow turbulence and boundary layer gradient were fine-tuned using roughness elements and spires. <h3>E2. Downburst Profile Development</h3> An impinging-jet style downburst is generated at the WindEEE dome through the release of pressure from a plenum above the testing chamber. The plenum is pressurized with six large fans for an adjustable amount of time or until a certain pressure is achieved. The built pressure then releases through a bell mouth with variable orifice sizes, D, to achieve a rapid downdraft of air. Given WindEEE’s unique 3-D test chamber, measurements were taken at various angles, theta, and radius, r, from the centre of the bell mouth. Commonly, these measurement locations are indicated by a non-dimensional parameter, r/D, and the angle, &thetasym; (theta). <h3>E3. Combined Downburst and ABL Profile Development</h3> With the unique capability of the WindEEE test chamber, profile measurements were taken while operating various combinations of the ABL and downburst-like flow configurations. The natural occurrence of a downburst in a storm acted as a driver for this segment of profile development. <h3>E4. Downburst with Radial Trip Profile Development</h3> For this test, wooden trips about 15cm tall were evenly placed all around the edge of the turntable. The downburst-like flow was generated similar to the downburst profile development section.   <h2>S2. 1-100 Scaled 50m Lattice Tower Model (T50)</h2> This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below. <h3>E1. ABL Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1. <h3>E2. Downburst Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2. <h3>E3. Combined Downburst and ABL Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3. <h3>E4. Downburst with Radial Trip Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 4.   <h2>S3. 1-100 Scaled 90m Lattice Tower Model (T90)</h2> This specimen included a triangular lattice tower of 50m built to a scale of 1:100. The model was made from a mixture of stainless-steel tubing for the spines and bracing elements while the joints were made from 3D printed PolyJet material. The models were fastened to a steel base measuring 14 by 14cm that is 1.27cm thick. A gradual ramp that sloped at a 1:12 angle was placed around this base extending 30.48cm. The model was placed on a mobile setup to be placed in experiment specific locations, as described below. <h3>E1. ABL Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 1. <h3>E2. Downburst Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 2. <h3>E3. Combined Downburst and ABL Wind Load</h3> The described specimen was tested under wind profiles developed from Specimen 1, Experiment 3.   <h2>S4. Aerodynamic lattice tower sectional model</h2> This specimen included a 1 m tall section of the top of the 50 m tower at a scale of 1:10. It is constructed of brass, steel, 3D printed nylon, PolyJet 3-D printed material, and steel screws. The antennas, railing and central ladder are all removable. The model was mounted on a rig made up of a 12.7 cm diameter steel pipe and a wooden base plate. The rig stands 60cm tall so that the model is above the sheared surface flow. The base and top plates of the rig were 90cm in diameter. The experiments were performed with three different configurations of the model. <h3>E1. Aerodynamics of the Bare structure without Top Plate</h3> During this test, the model was measured as a bare structure (no antennas, ladders, or other components). The model was tested under ABL flow to outline the aerodynamic effects of the baseline model. <h3>E2. Aerodynamics of the Structure with Top Plate</h3> During this test, a top plate hovered over the model for the entirety of the test program. This plate encourages 2-D flow properties in ABL flow to mimic aerodynamic properties seen in horizontal testing in traditional wind tunnels. <h3>E3. Aerodynamics of the Structure with Ancillary Components</h3> During this test, ancillary components including ladders, railing, and antenna were attached to the model. These items act to increase the frontal area of the model which are expected to change the aerodynamic properties of the model.   Note: Given the number of data files captured in this program, the files required to be uploaded in compressed '.zip' folders.

opencc-by-4.0May 2022View details →

Radar-based severe thunderstorm classification in Switzerland from 2016-2021

The data is owned by MeteoSwiss and the Laboratoire de Télédétection Environnementale, EPFL. It is linked to the research article: Feldmann et al., 2022, Hailstorms and rainstorms versus supercells - a regional analysis of convective storm types in the Alpine region, submitted to npj Climate and Atmospheric Science. Whenever using the dataset, please include a reference to this article. This dataset contains thunderstorm track data of thunderstorms severe thunderstorms in Switzerland from 2016-2020. Tracked variables include time, position, size, translational speed and several intensity variables. The README.txt file contains a comprehensive list of all included variables and their units. The data is provided as a  .txt file. The methods used to obtain this data are described in the corresponding publication (Feldmann et al., 2022).

opencc-by-4.0Dec 2022View details →

Data for figures 2 till4 of "Small-scale discharges observed near the top of a thunderstorm"

The data as well as the .gle files for generating figures 2 till 4 of the paper "Small-scale discharges observed near the top of a thunderstorm" by O.~Scholten, B.~M.~Hare, J.~Dwyer, N.~Liu, C.~Sterpka, J.~Assink, H.~Leijnse, S.~ter Veen submitted to JGR, see <a href="https://www.essoar.org/doi/10.1002/essoar.10512414.1">https://www.essoar.org/doi/10.1002/essoar.10512414.1</a> for an earlier version of this work

opencc-by-4.0Nov 2018View details →

Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data

FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper: "Evaluation of machine learning classifiers for predicting deep convection" by Peter Ukkonen and Antti Mäkelä (to appear in JAMES) The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python.

opencc-by-4.0Jan 2021View details →

Thunderstorm_Lower_Positive_Charge_Data

Data and instructions on how to produce the figures in an article titled "Thunderstorm lower positive charge and triggered negative lightning-leaders" to be submitted to the Journal of Geophysical Research, Atmospheres. Triggered negative leaders propagated into thunderstorm lower positive charge.  Current measured at the ground where the triggering wire was attached and an estimate of the volume occupied by the branched leaders give an average charge density in the lower positive region.

opencc-by-4.0Dec 2023View details →

Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year cloud-to-ground lighting dataset"

Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year"

opencc-by-4.0Apr 2022View details →

Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN

This dataset is for the article titled "Thunderstorm activity over the Qinghai–Tibet Plateau indicated by the combined data of the FY-2E geostationary satellite and WWLLN" which is being submitted to the Remote Sensing for review.  Abstract: Thunderstorm activity over the Qinghai–Tibet Plateau (QTP) has important climatic effects and disaster impacts. Using the thunderstorm feature dataset (TFD) established based on the black body temperature (TBB) and cloud classification (CLC) products of the Fengyun-2E (FY-2E) geostationary satellite, as well as the lightning data of the World Wide Lightning Location Network (WWLLN), the temporal and spatial distributions and some cloud properties of the thunderstorms over the QTP were analyzed. Approximately 93.9% and 82.7% of thunderstorms over the QTP occur from May to September and from 12 to 21 o'clock local time, and the corresponding peaks are in August and at 14:00, respectively. There are three centers featuring frequent thunderstorms in the southeast, south-central, and southwest regions of the QTP. The average thunderstorm cloud area (the region with TBB ≤ −32℃) is 1.8 × 104 km2. Approximately 32.9% of thunderstorms have strong convective cells (SCCs) composed of areas with TBB ≤ −52℃.The average number and area ratio of SCCs are 3.6 and 25.4%, respectively, and their spatial distribution is given. The average cloud area and the number and area ratio of SCCs of extreme-lightning thunderstorms (thunderstorms with the top 10% of lightning numbers) are approximately 30.0, 3.9, and 1.5 times those of normal thunderstorms. The spatial distribution of the thunderstorm activity is quite different from that of lightning activity given by the Lightning Imaging Sensor (LIS) and Optical Transient Detector (OTD) over the northeastern and southwestern QTP, which may mean that the convection intensity, cloud structure, and charge structure of the thunderstorms over the QTP are different between different regions and seasons.

opencc-by-4.0Sep 2022View details →

STORM973: Understanding of Thunderstorm and Lightning in the Beijing Metropolitan Region

The dataset used in this study is consist with observation and model results. Lightning data is obtained from regional Beijing Broadband Lightning Network (BLNet) comprised by 16 stations, which could detect the total lightning including intra-cloud lightning and cloud-to-ground lightning with lightning information of location, polarity, type, height and detection error. The average detection efficiency for total flashes is 93.2%, and the identification efficiency of BLNET for CG flashes is 73.9%. Radar data is obtained from the operational radar network of five operational S-band or C-band Doppler radars around Beijing area with the horizontal resolution of 0.01˚ (latitude) × 0.01˚ (longitude). The version of WPSv4.1.2, WRFDAv4.1.2 and WRFv4.1.2 are used in this study. Lightning data assimilation scheme (LDA) was proposed based on the relationship between lightning and updraft. The lightning data used for assimilation is provided in the dataset. The dataset volume is 85M.

opencc-by-4.0Jul 2024View details →

Contrast in Sprite and Elve productions from a coastal thunderstorm in eastern Spain [Dataset]

<a name="_Hlk172282785"></a>Data for the study on the production conditions of 58 sprites and 14 elves observed during a storm over the eastern Mediterranean coast of Spain during the night of November 27 to 28, 2016: optical data from two sensitive cameras, characteristics of the lightning activity and cloud top temperature from the radiometer onboard the Meteosat Second Generation satellite reveal striking contrasts regarding the two types of phenomena.